Mercedes Jimenez

Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished. Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer, NBEATS and Echo State Network.

Feature selection is a widely studied technique whose goal is to reduce the dimensionality of the problem by removing irrelevant features. It has multiple benefits, such as improved efficacy, efficiency and interpretability of almost any type of machine learning model. Feature selection techniques may be divided into three main categories, depending on the process used to remove the features known as Filter, Wrapper and Embedded. Embedded methods are usually the preferred feature selection method that efficiently obtains a selection of the most relevant features of the model. However, not all models support an embedded feature selection that forces the use of a different method, reducing the efficiency and reliability of the selection. Neural networks are an example of a model that does not support embedded feature selection. As neural networks have shown to provide remarkable results in multiple scenarios such as classification and regression, sometimes in an ensemble with a model that includes an embedded feature selection, we attempt to embed a feature selection process with a general-purpose methodology. In this work, we propose a novel general-purpose layer for neural networks that removes the influence of irrelevant features. The Feature-Aware Drop Layer is included at the top of the neural network and trained during the backpropagation process without any additional parameters. Our methodology is tested with 17 datasets for classification and regression tasks, including data from different fields such as Health, Economic and Environment, among others. The results show remarkable improvements compared to three different feature selection approaches, with reliable, efficient and effective results.

The aims of this research were twofold: first, to compare the levels of organisational harmony between family and non-family firms and, second, to study the influence of organisational harmony on family firms’ performance (profitability, longevity and group cohesion). Starting from a definition of organisational harmony as a value and considering the importance of the management of organisational values, we use the main topics indicated by the general literature (organisational climate, trust and participation) to analyse organisational harmony, as well as three other topics for performance (profitability, survival and group cohesion). Results indicate that family firms have higher levels of these three qualities than non-family firms, and that the levels of trust, participation and organisational climate have a positive and significant influence on the performance of family firms. These results can be understood as a consequence of the influence of family social capital on organisational social capital and so on the performance of family businesses. From the perspective of institutionalism, the higher levels of harmony existing in family businesses are a reflection of the pressures that the owning family as an institution exercises on the organisational social capital in its companies

The goal of this study is analyse the influence of perceived supervisor support (PSS) by employees at a micro level and the role of the cultural values of “power distance” and “masculinity” at a macro level on direct employee participation in decision making (PDM). Furthermore, the influence of the gender of managers and employees is taken into account. The analysis is based upon the Sixth European Working Conditions Survey carried out by Eurofound in 2016. The results of a Hierarchical linear model indicate that all predictors significantly influenced PDM and PSS positively and cultural values negatively. When the gender of managers and employees is considered, the findings suggest that PSS has a larger impact on PDM when male managers address female employees. Regarding the moderating effect of PSS on cultural values, it is shown that masculinity and power distance lose importance when employees have the support of their supervisors.